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Skippable video advertising offers users the option to skip directly to their desired video content after viewing a limited amount of advertising and is becoming a format increasingly preferred by advertisers. This study constructed a mediator model based on advertising attitude to examine the influence of users' perceived control and perceived intrusiveness on brand attitude and advertising avoidance intention in skippable video ads.

The study considered the structural equation model using a self-reporting measure method. The research model was tested using a sample of 302 respondents.

The results showed that perceived control positively affected ad and brand attitude and negatively affected advertising avoidance intention. Perceived intrusiveness negatively affected attitudes toward the advertising and the brand but positively affected advertising avoidance intention. Ad attitude and brand attitude had no significant influence on advertising avoidance intention. The results also confirmed that ad attitude mediates the path from perceived control and intrusiveness to brand attitude.

This study further enriches the theoretical development of skippable video ads and expands the perspective and scope of interactive advertising research.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

The study investigates the antecedents and consequences of digital ad avoidance in the e-commerce industry. This study aims to investigate how digital ad avoidance relates to internet usage over time, using a two-wave longitudinal research design. This study also explores how real-world advertising clutter (clutter and non-clutter) affects these relationships.

The data were collected from 192 online consumers in Türkiye, and a two-wave longitudinal research design and structural equation modeling were conducted to test the hypotheses of the conceptual model.

The results indicated that perceived goal impediment and prior negative experience had positive effects on affective avoidance in both ad-clutter and non-clutter periods. Further, affective avoidance had a significant positive effect on both cognitive and behavioral avoidance in either period. In addition, ad clutter moderated the effects of cognitive and behavioral avoidance on online purchase intention.

This research provides new evidence of the crucial role perceived goal impediment, prior negative experience and perceived control each play in driving digital ad avoidance. This paper contributes to the body of knowledge in the field by exploring how advertising clutter (both cluttered and non-cluttered settings) moderates these relationships through a two-wave longitudinal study.

This study aims to investigate the effects of ball–material ratio on the properties of mixed powders and Cu-Bi self-lubricating alloy materials.

Cu-Bi mixed powder was ball milled at different ball–material ratios, and the preparation of Cu-Bi alloy materials was achieved through powder metallurgy technology. Scanning electron microscopy, X-ray diffraction and Raman spectroscopy were conducted to study the microstructure and phase composition of the mixed powder. The apparent density and flow characteristics of mixed powders were investigated using a Hall flowmeter. Tests on the crushing strength, impact toughness and tribological properties of self-lubricating alloy materials were conducted using a universal electronic testing machine, 300 J pendulum impact testing machine and M200 ring-block tribometer, respectively.

With the increase in ball–material ratio, the spherical copper matrix particles in the mixed powder became lamellar, the mechanical properties of the material gradually reduced, the friction coefficient of the material first decreased and then stabilized and the wear rate decreased initially and then increased. The increase in the ball–material ratio resulted in the fine network distribution of the Bi phase in the copper alloy matrix, which benefitted its enrichment on the worn surface for the formation a lubricating film and improvement of the material’s tribological performance. However, a large ball–material ratio can excessively weaken the mechanical properties of the material and reduce its wear resistance.

The effects of ball–material ratio on Cu-Bi mixed powder and material properties were clarified. This work provides a reference for the mechanical alloying process and its engineering applications.

Wearable health devices (WHDs) have demonstrated significant potential in assisting elderly adults with proactive health management by utilizing sensors to record and monitor various aspects of their health, including physical activity, heart rate, etc. However, limited research has systematically explored older adults’ continued usage intention toward WHD. By utilizing the extended unified theory of acceptance and use of technology (UTAUT2), this paper aims to probe the precursors of elderly adults’ continuance intention to use WHD from an enabler–inhibitor perspective.

The research model was developed based on UTAUT2 and examined utilizing the partial least squares technique (PLS). The research data were collected through in-person meetings with older people (n = 272) in four cities in China.

Results reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic values and perceived complexity are the positive predictors of elderly adults’ continuance intention to use WHDs. Technology-related anxiety and usage cost negatively influence the formation of older people’s continuance intention.

This work is an original empirical investigation that draws on several theories as guiding frameworks. It adds to the existing literature on the usage of wearable technologies and offers insights into how the elderly’s intentions to continue using WHDs can be developed. This study broadens the scope of the UTAUT2 application and presents an alternative theoretical framework that can be utilized in future research on the usage behavior of wearable devices by individuals.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

Innovative modern technologies are utilised in the hospitality and tourist industry to attract prospective consumers. Travel vlogs have emerged as a type of visual travel blog which includes information regarding tourist destinations, accommodation, cuisine, touristic activities, mode of accessibility, etc. The current study aims to investigate the influence of vlogger credibility on triggering the tourist intention to visit an eco-tourism destination.

The present study is a quantitative study, and it was conducted by surveying 218 respondents. A structured, close-ended and self-administrated questionnaire was used to collect data from the respondents.

The findings indicated that vlogger trustworthiness, expertise and attractiveness significantly impact Bangladeshi tourists' intention towards visiting an eco-tourist destination.

The present study has several theoretical and practical contributions. Theoretically, the research framework illustrated how the vloggers' trustworthiness, expertise and attractiveness on Bangladeshi tourists' visit intention to eco-tourist destinations. Practically, tourism industry practitioners, particularly destination marketers, will get benefit from the present study.

Drawing on anthropomorphism and the stereotype content model, this study aims to explore the impacts of robot anthropomorphism on hotel guests’ word of mouth and continuous usage intention through perceived warmth and competence. This study also investigates the moderating effects of guests’ expertise.

A total of 524 valid data was collected with Chinese hotel guest through an online survey. Data were analyzed by using structural equation modeling.

The results indicate that robot anthropomorphism positively affects perceived warmth and competence. Perceived warmth positively affects guests’ word of mouth and continuous usage intention, while perceived competence only positively influences guests’ continuous usage intention. The findings further show that guest expertise weakens the positive relationship between robot anthropomorphism and perceived warmth and competence.

This study advances robot service literature by integrating the stereotype content model with robot anthropomorphism in exploring hotel guests’ perceptions on service robots. This study also discovers the vital boundary role of guest characteristics in human–robot interaction. This study provides valuable information for hoteliers to design and adopt better robots for optimal guest experiences.

基于拟人化和刻板印象内容模型, 本研究探讨了机器人拟人化通过感知温暖和感知能力对酒店客人的口碑和持续使用意向的影响。此外, 本研究也探讨了客人专业知识的调节作用。

通过在线调查问卷, 我们共收集了来自中国酒店客人的524份有效数据, 并采用结构方程模型对这些数据进行了分析。

研究结果表明, 机器人拟人化积极影响感知温暖和感知能力。感知温暖会对客人的口碑和持续使用意向产生积极影响, 而感知能力只对客人的持续使用意向产生积极影响。研究结果进一步表明, 客人的专业知识会削弱机器人拟人化与感知温暖和能力之间的积极关系。

为了探索酒店客人对服务机器人的感知（状况/情况）, 本研究将机器人拟人化因素整合到刻板印象内容模型中, 其结果推动了机器人服务研究的发展。本研究还发现了客人特征在人机互动中的重要边界作用。本研究为酒店经营者设计和采用更好的机器人以获得最佳的客人体验提供了有价值的参考。

Se recopiló datos válidos de un total de 524 clientes de hoteles chinos mediante una encuesta en línea. Los datos fueron analizados mediante un modelo de ecuaciones estructurales.

Basándose en el antropomorfismo y en el modelo de contenido estereotipado, este estudio explora el impacto del antropomorfismo de los robots en el boca a boca de los clientes del hotel y en la intención de uso continuado a través de la calidez y la competencia percibidas. El estudio investiga también los efectos moderadores de la experiencia de los huéspedes.

Los resultados indican que el antropomorfismo del robot afecta positivamente a la calidez y a la competencia percibidas. La calidez percibida influye positivamente en el boca a boca y en la intención de uso continuado de los huéspedes, mientras que la competencia percibida sólo influye positivamente en la intención de uso continuado de los huéspedes. Los resultados muestran, además, que la experiencia del cliente debilita la relación positiva entre el antropomorfismo del robot y la percepción de calidez y de competencia.

Este estudio supone un avance en la literatura sobre robots de servicio al integrar el modelo de contenido estereotipado con el antropomorfismo de los robots, en la exploración de las percepciones de los huéspedes de hoteles sobre los robots de servicio. Este estudio también muestra el papel vital que desempeñan las características de los huéspedes en la interacción humano-robot. El estudio proporciona información valiosa para que los hoteleros diseñen y adopten mejores robots para ofrecer experiencias óptimas a los huéspedes.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.